Instructions to use DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/fnet-large-Financial_Sentiment_Analysis_v3") - Notebooks
- Google Colab
- Kaggle
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README.md
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This is a sentiment analysis (text classification) model concern comments about finances.
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https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb
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## Intended uses & limitations
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This is a sentiment analysis (text classification) model concern comments about finances.
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb
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## Intended uses & limitations
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